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This paper combines the multi-innovation theory with the auxiliary model identification idea to present the auxiliary model based multi-innovation stochastic gradient algorithm by expanding the scalar innovation to an innovation vector and introducing the innovation length. Convergence analysis in the stochastic framework indicates that the parameter estimation error consistently converges to zero...
For pseudo-linear regression identification models corresponding output error systems with colored measurement noises, a difficulty of identification is that there exist unknown inner variables and unmeasurable noise terms in the information vector. This paper presents an auxiliary model based multi-innovation stochastic gradient algorithm by using the auxiliary model technique and by expanding the...
This paper applies the multi-innovation identification theory to study the parameter estimation problem for CARMA models, and presents the multi-innovation extended stochastic gradient algorithm by expanding the scalar innovation to an innovation vector, and analyzes and proves the convergence properties of the algorithms involved. The simulation results show that the proposed algorithms are effective.
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